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% 中英文摘要
\begin{cabstract}
本文主要研究社交网络中的影响最大化问题。当前，影响最大化问题主要研究方向在于如何提高信息传播模型拟合现实信息传播的能力，如何降低影响最大化算法的时间复杂度以及如何提高算法的效果。为了解决上述第一个问题，学者们提出了许多不同的信息传播模型，其中最常用的为独立传播模型。然而独立传播模型没有考虑到用户发布信息的本身因素，使得其在社交网络中的效果较差。针对上述第二个问题，学者们通常将种子节点个数作为算法的输入，而没有给出该输入应该如何选择。

针对上述问题，本文的研究工作主要有两方面：

第一，本文提出了基于情绪的独立传播模型。在传统的独立传播模型中，不同信息在相同边中的传播概率是相同的，这不符合社交网络信息传播的一般规律。在社交网络中，用户发布的信息通常蕴含着用户的情绪。不同情绪对其他用户的影响能力不同，本文认为信息是情绪的载体，情绪传播是信息传播的本质。结合上述假设，我们在独立传播模型中考虑用户情绪的因素，将边的传播概率看作是不同情绪传播概率的加权组合，提出了情绪传播模型。该模型由于考虑信息本身的情绪因素，对现实社交网络的信息传播过程拟合得更好且对信息传播的预测能力也更好。本文在微博数据集上的实验验证了上述结论。

第二，本文研究了如何选择影响最大化问题的种子节点个数。当前的影响最大化问题都将种子节点个数取值为50而没有给出具体原因。事实上在实际的影响最大化应用中，如何选择种子节点个数具有重要意义。例如在社交网络中的在线营销，若种子节点个数选择太少，则会造成营销效果不佳；若种子节点个数选择太多，则会造成预算的浪费。因此，研究如何选择种子节点个数对用户具有指导意义。本文提出选择最优种子节点个数的方法，并通过在大量人工生成的随机网络和现实网络中的实验，研究最优种子节点个数的影响因素，包括信息传播模型的传播概率、网络规模以及网络平均度等。

最后，本文实现了一个社交影响最大化分析系统。该系统是上述两部分研究部分的直接应用，由情感分类模块、模型拟合与计算模块以及结果展示模块三部分组成。系统通过用户输入的网络和信息传播历史轨迹，给出情绪传播模型的估计参数以及种子节点个数推荐值，并以友好的界面展示。

\end{cabstract}

\begin{eabstract}

We study the influence maximization problem in social networks. The main research fields within this task are to improve the information propagation model for fitting the real-world episodes, to reduce the time complexity and to improve the efficiency of influence maximization algorithms. Researchers propose many different information propagation models, where independent cascade model is the one of the most frequently used. For the second field, researchers usually regard the number of seed nodes as the input of influence maximization problem without pointing out how to choose the value of it.

Focusing on these problems, we first propose an emotion-based independent cascade model. In the original model, different information has the same diffusion probability through the same edge, which doesn't meet the law of information propagation in social networks. In social networks, the information user post often contains emotions. We believe that information is the carrier of emotions and emotion propagation is the nature of information propagation. Based on this assumption, we propose an emotion-based independent cascade model, which takes emotions into it. The experimentations in this paper show that our model is better in fitting and predicting the real-world episodes because of considering the emotions. 

We also study on how to choose the number of seed nodes in influence maximization problem. All the researchs use 50 as the number without pointing out why. Actually, how to choose this number is meaningful in most of real-world applications. Consider an online marketing example, the performance of e-marketing will be bad if the number of seed nodes is too small. On the contrary, we may waste much budget if the number of seed nodes is too large. In this paper, we propose a way to find the optimal number of seed node. And through numerical experimentations, we find out the relationships between the optimal number of seed nodes and other factors, including the diffusion probability of information progation model, the scale of network and the average degree of network.

Finally, we implement a analysis system for influence maximization in social networks with the methods proposed by us. 
\end{eabstract}